{"id":9103,"date":"2025-12-15T20:41:11","date_gmt":"2025-12-15T23:41:11","guid":{"rendered":"https:\/\/aaep.org.ar\/?p=9103"},"modified":"2025-12-15T20:41:13","modified_gmt":"2025-12-15T23:41:13","slug":"understanding-and-predicting-recidivism-in-latin-america-insights-from-machine-learning-and-policy-oriented-research","status":"publish","type":"post","link":"https:\/\/aaep.org.ar\/?p=9103","title":{"rendered":"Understanding and Predicting Recidivism in Latin America: Insights from Machine Learning and Policy-Oriented Research"},"content":{"rendered":"<iframe loading=\"lazy\" class=\"wonderplugin-pdf-iframe\" src=\"https:\/\/aaep.org.ar\/wp-content\/plugins\/wonderplugin-pdf-embed\/pdfjslight\/web\/viewer.html?v=2&file=https:\/\/aaep.org.ar\/works\/works2025\/20.pdf\" width=\"100%\" height=\"600px\" style=\"border:0;\"><\/iframe>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. This study addresses that gap through three contributions. First, we review the individual, institutional, and environmental determinants of recidivism with attention to Latin American contexts. Second, drawing on two decades of Argentine prison data, we characterize recidivism patterns and apply six machine learning models to predict reoffending. Economic offenses and age at incarceration emerge as the strongest predictors, while geographic indicators also matter, given the clustering of repeat offenders in certain prisons. We show that prison-level information, often collected but underused, enables reasonably accurate risk prediction and can guide more effective rehabilitation and prison management. Third, we discuss how AI-based prediction tools could be applied by judges, correctional authorities, and policymakers, as well as the institutional, data, and ethical challenges such implementation entails.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[29],"tags":[33],"class_list":["post-9103","post","type-post","status-publish","format-standard","hentry","category-anales","tag-aaep-anales-2025"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Understanding and Predicting Recidivism in Latin America: Insights from Machine Learning and Policy-Oriented Research - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Understanding and Predicting Recidivism in Latin America: Insights from Machine Learning and Policy-Oriented Research - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"og:description\" content=\"Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. 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